Robotic hex-nut sorting system with deep learning

Cristian Almanza, Javier Martínez Baquero, Robinson Jiménez-Moreno


This article exposes the design and implementation of an automation system based on a robotic arm for hex-nut classification, using pattern recognition and image processing. The robotic arm work based on three servo motors and an electromagnetic end effector. The pattern recognition implemented allows classifying three different types of hex-nut through deep learning algorithms based on convolutional neural network architectures. The proposed methodology exposes four phases: the first is the design, implementation, and control of a robotic arm. The second is the capture, classification, and image treatment; the third allows gripping the nut through the robot’s inverse kinematic. The final phase is the re-localization of the hex-nut in the respective container. The automation system successfully classifies all the types of hex-nuts, where the convolutional network used is an efficient and recent pattern recognition method, with an accuracy of 100% in 150 iterations. This development allows for obtaining a novel algorithm for robotic applications in hex-nut sorting


convolutional network; hex-nut classification; image processing; robotic arm;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578